What remains to consider is the computational complexity. As discussed, explicitly storing and updating the weighted vectors \(\mathbf {w}^k_u\)  for each node \(u\)  is not feasible because even for small \(k\)  this might lead to memory usage of \(O(n^2)\) . Instead, we will efficiently detect a node \(x\)  from a sketch of \(u\) 's \(k\) -hop weighted neighborhood frequency vector \(\mathbf {w}_u^k\)  for which it holds \(\mathbf {w}^k_u[x] \ge t\) . In this case the sketch will be realized using a frequent items mining algorithm such as [1]} which detects heavy hitters in streams of weighted items without explicitly storing all items.
